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OPEN High frequency oscillations in epileptic and non‑epileptic human during a cognitive task Martin Pail 1*, Jan Cimbálník2, Robert Roman1,3, Pavel Daniel1,3, Daniel J. Shaw3,4, Jan Chrastina5 & Milan Brázdil1,3

Hippocampal high-frequency electrographic activity (HFOs) represents one of the major discoveries not only in epilepsy research but also in cognitive science over the past few decades. A fundamental challenge, however, has been the fact that physiological HFOs associated with normal function overlap in frequency with pathological HFOs. We investigated the impact of a cognitive task on HFOs with the aim of improving diferentiation between epileptic and non-epileptic hippocampi in humans. Hippocampal activity was recorded with depth electrodes in 15 patients with focal epilepsy during a resting period and subsequently during a cognitive task. HFOs in ripple and fast ripple frequency ranges were evaluated in both conditions, and their rate, spectral entropy, relative amplitude and duration were compared in epileptic and non-epileptic hippocampi. The similarity of HFOs properties recorded at rest in epileptic and non-epileptic hippocampi suggests that they cannot be used alone to distinguish between hippocampi. However, both ripples and fast ripples were observed with higher rates, higher relative amplitudes and longer durations at rest as well as during a cognitive task in epileptic compared with non-epileptic hippocampi. Moreover, during a cognitive task, signifcant reductions of HFOs rates were found in epileptic hippocampi. These reductions were not observed in non-epileptic hippocampi. Our results indicate that although both hippocampi generate HFOs with similar features that probably refect non-pathological phenomena, it is possible to diferentiate between epileptic and non-epileptic hippocampi using a simple odd-ball task.

Te discovery of high-frequency electrographic activity represents one of the essential milestones not only in epilepsy research, but also in cognitive science over the past few decades. Tese transient high and very high-frequency oscillations (HFOs/VHFOs) in invasive EEG (stereoelectroencephalography; SEEG) have been recorded repeatedly in several allocortical and neocortical structures. Tese short-lasting feld potentials, both ictal and interictal phenomena, can be divided further into “ripples” (80–250 Hz), “fast ripples” (250–600 Hz), “very fast ripples” (VFR; 600–1000 Hz), and “ultra-fast ripples” (UFR; 1–2 kHz), all of which have been studied widely in humans under physiological and pathological conditions­ 1–12. HFOs are believed to stem from the short-term synchronization of neuronal populations and their activity, and it appears that they are connected to normal as well as pathological brain ­functions6,13. While physiological HFOs seem to represent summated synchronous inhibitory postsynaptic potentials (IPSP) generated by interneu- ronal cell subpopulations regulating the principal cell activity and their discharges­ 14. Epileptic HFOs might refect the feld potentials which are formed by the activity from clusters of abnormal synchronously bursting pyramidal cells, generating population spikes, and decreased inhibitory interneuron ­fring6,15. Te detected HFO frequency can be determined purely from the behavior and activity of one cell subpopulation (“pure” HFOs). However, the observed HFOs (especially beyond the physiologic limits of neuronal fring; > 300 Hz), may also represent the net frequency of neuronal populations, more specifcally due to the activity of diferent cell subpopulations of

1First Department of Neurology, Brno Epilepsy Center (Full member of the ERN EpiCARE), St. Anne’s University Hospital and Medical Faculty of Masaryk University, Pekařská 53, Brno 65691, Czech Republic. 2International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic. 3CEITEC – Central European Institute of Technology, Masaryk University, Brno, Czech Republic. 4School of Life and Health Sciences, Aston University, Birmingham, UK. 5Department of Neurosurgery, Brno Epilepsy Center, St. Anne’s University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic. *email: [email protected]

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synchronized neurons between which is a phase delay in activity. Each cell assembly then usually oscillate with a lower frequency than observed (“emergent” HFOs)16,17. Tere is evidence that ripple generation is infuenced by larger networks; smaller networks are involved in fast ripples and even less in VFR and UFR generation, which can be observed ­focally9. Interest in HFOs is related primarily to the localization of the epileptogenic zone, since they are considered as being more focal and specifc than classical epileptic ­spikes18 that are only partially concordant with the epileptogenic ­zone19. As HFO rates are higher in focal seizure-generating tissue, they have attracted as a possible clinical biomarker­ 3. Unfortunately, pathological and physiological HFOs cannot be distinguished by rate of their occurrence, as some regions are identifed as generators of physiological ­HFOs12. Moreover, another signifcant problem, has been the fact that pathological HFOs overlap in frequency with physiological HFOs associated with normal brain ­function8,20–22. How these two types of electrographic phenomena can be separated remains ­unclear6, as frequency and/or amplitude analysis alone seem to be insufcient for their ­delineation23–25. Terefore, the translation of HFOs into clinical practice is hindered by the inability to diferentiate between pathological and normal HFOs in SEEG recordings. And so, more than twenty years afer their discovery, there still exist questions about HFOs as biomarkers of epileptogenic and epileptogenic zones, and about their utility in clinical practice­ 12,26,27. Until now, various analytical approaches have been sought to distinguish pathological and physiological HFOs. Te methods used most commonly are based on, for example, a specifc regional distribution in mesial temporal ­structures28; the association of HFOs with epileptiform discharges­ 4,26,29, slow waves­ 30 o spindles­ 31, or the diference between HFOs produced spontaneously and those induced by a cognitive ­task8,23,24. In the recent study of Sakuraba et al., epileptogenic region was determined based on less suppressive efect of REM sleep on HFOs in contrast to non-epileptogenic/physiological region­ 32. Some authors have suggested separating HFOs by clustering them based on features such as frequency, duration, and ­amplitude24,25,33. Other reports have addressed this problem proposing several methods for dissociating diferent origins based on the width of the spectral frequency content of individual events, number of distinct cycles observed, and the presence of actual oscillations in the unfltered raw ­signal34,35. Finally, when investigating electrophysiological brain recordings, the term HFO should be used to describe true high-frequency local feld potential oscillations in the invasive EEG, that is oscillations visible in the raw recording and not the high-frequency Fourier components from a bandpass ­flter36. Te ability to distinguish between pathological and physiological HFOs is crucial for understanding normal cognitive functions and no less important for the translation of HFOs into clinical practice. In a recent study, we tested the hypothesis whether the presumed efect of cognitive task on hippocampal ripples can be used as a new approach for distinguishing pathological HFOs in the epileptic hippocampus (EH) from physiological HFOs in the non-epileptic hippocampus (NEH)8. To diferentiate them, a simple oddball task was used. Tis study revealed diferent and, in some cases, opposing behavior of ripples within EH and NEH: Ripples were signifcantly more reduced during a cognitive task than in a resting period in EH, but in NEH this diference remained statistically ­marginal8. Moreover, we observed a signifcant suppression of ripple rate in the frst second afer stimulus onset only in ­NEH8. Importantly, however, we did not examine fast ripples due to a low sampling frequency. In the present study, we tested the hypothesis that not only ripples, but also fast ripples are modulated by cognitive tasks. We aimed to fnd a distinct impact of a cognitive task on HFOs (the rate and other HFO char- acteristics) within EH and NEH. To test the hypothesis, we analyzed hippocampal SEEG of 15 patients during resting period and during a simple cognitive oddball task. Methods Subjects. In our study we included 15 patients (7 females) ranging in age from 24 to 56 (mean: 38.3 ± 9.3) years. All patients sufered from medically intractable focal temporal epilepsies. For demographic and clinical characteristics of the included subjects, see Table 1. In most patients, chronic anticonvulsant medication was reduced slightly for the purposes of video-SEEG monitoring. Te study procedures were approved by Masaryk University and St. Anne’s University Hospital Ethics Committees. All subjects gave their written informed con- sent prior to the study investigation. All methods were performed in accordance with the relevant guidelines and regulations.

EEG recordings. Patients underwent the implantation of depth electrodes as part of their evaluation for pharmacoresistant focal epilepsy in order to localize seizure origin prior to surgical treatment. Te location of the implanted electrodes was determined by clinical requirements. Each patient received 3–14 intracerebral electrodes containing either 5, 8, 10 or 15 individual contacts, in the and facultatively in other brain lobes using the Talairach stereotaxic system­ 37. Standard platinum depth electrodes (ALCIS) were used (diameter = 0.8 mm; inter-contact distance = 1.5 mm, contact surface area = 5 mm2; contact length = 2 mm). Afer implantation, each patient underwent MRI scanning to localize electrode placement. We used a 192-chan- nel research EEG acquisition system (M&I; Brainscope, Czech Republic) for recording 30 min of an awake rest- ing interictal period as well as the cognitive task. Te sampling rate was 25 kHz and dynamic range of ± 25 mV with 10 nV (24 bits). Te EEGs were low-pass fltered and downsampled to 5 kHz for further processing. All recordings were referenced to the average of intracranial signals. EEG data from a total of 111 electrode contacts positioned in either epileptic hippocampi (76) or non-epileptic hippocampi (35) were investigated (Table 1). No other structures were analyzed for the presence of spikes and HFOs. In identifying epileptic (EH) and non- epileptic hippocampi (NEH), we followed a process similar to that reported ­elsewhere8,24—specifcally, based on the results of a standard visual analysis of interictal and ictal SEEG recordings: EH were identifed by the pres- ence of a seizure onset zone (confrmed by recording multiple seizures): the site in which contacts showed the frst EEG ictal activity, with characteristic desynchronization and low voltage fast activity pattern. As presented

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Number of analyzed Postoperative Number of Number of events Age at outcome Engel analyzed analyzed Number of in NEH Age at Seizure MRI before Side of Intervention/ (follow-up, contacts contacts in analyzed events in (spikes/R/ Subject Gender SEEG FS onset SEEG epilepsy SOZ histopathology year) in EH NEH EH (spikes/R/FR) FR) Lef hip- Lef AMTR/ 1 F 26 – 17 Normal Lef IA (5) 6 (lef) 3 (right) 1950/517/835 205/69/302 pocampus FCD IB Right hip- Right hip- Right AMTR/not 2 F 56 – 28 pocampal Right IIIA (5) 6 (right) – 1623/1991/1380 – pocampus available atrophy Lef hip- Lef hip- 3 M 40 – 1 pocampal Lef Lef AMTR/negat IA (5) 8 (lef) – 4501/2212/1454 – pocampus atrophy Hippocam- pus bilater- 4 M 38 – 27 Normal Bilaterally VNS – 3 (right) – 812/420/404 – ally (mainly right side) Focal hyperinten- Right hip- sity within Right AMTR/FCD 5 M 41 – 33 Right pocampus, IA (5) 7 (right) 3 (lef) 1735/672/623 499/209/286 right basal IIIb ganglioglioma lesion temporal lobe Posten- cephalitic Lef AMTR/hip- changes of Lef hip- pocampal sclerosis, 6 F 33 – 2 lef T lobe, Lef pocampus, IA (5) 6 (lef) 5 (right) 1241/1029/97 155/50/297 postencephalitic lef hip- lesion changes pocampal atrophy Bilateral Hippocam- hippocam- pus bilater- 7 M 35 – 21 Bilaterally VNS – 7 (right) – 2314/978/560 – pal atrophy, ally (mainly RX > LT right side) Hip- pocampus 8 M 37 FS 31 Normal Bilaterally bilaterally Lef AMTR/negat IA (4) 8 (lef) – 1149/1247/1002 – (mainly lef side) Lef hip- Lef hip- Lef AMTR/FCD 9 F 27 – 9 pocampal Lef IIIA (4) 7 (lef) – 2767/1878/1767 – pocampus IIIA atrophy Right hip- Right hip- Right AMTR/hip- 10 M 51 FS 2 pocampal Right IA (4) 5 (right) 7 (lef) 636/568/895 222/171/435 pocampus pocampal sclerosis atrophy Lef hip- pocampal atrophy, mild post- Lef hip- Lef AMTR/hip- 11 M 24 – 10 Lef IIA (4) 5 (lef) – 815/507/228 – traumatic pocampus pocampal sclerosis gliosis of lef pericen- tral region Asymetry Temporal Resection of of colateral pole and temporal pole and 12 F 33 – 29 sulci in Lef lateral anterior part of IIIA (4) – 5 (lef) – 308/69/125 temporal temporal lateral temporal lobe cortex cortex/negat Lef hip- 13 F 45 – 26 pocampal Lef Lef T pole Lef AMTR / negat IIIA (4) – 6 (right) – 155/53/328 atrophy Nodular heteroto- pia along dorsal part Lef lateral Lateral cortical 14 F 36 – 16 of lateral Lef cortex TO resection TO lef/ IIA (4) – 4 (lef) – 326/181/137 ventricle junction FCD IIA and lateral cortex TO lef Lef hip- Lef hip- 15 M 53 – 33 pocampal Lef Lef AMTR IA (1) 8 (lef) – 3560/1641/1064 – pocampus atrophy

Table 1. Demographic and clinical data. M = male, F = female; SEEG = stereoelectroencephalography; T = temporal; O = occipital; FS = febrile seizures; SOZ = seizure onset zone; AMTR = anteromedial temporal resection; FCD = focal cortical dysplasia; EH = epileptic hippocampus; NEH = non-epileptic hippocampus; VNS = vagus nerve stimulation, R = ripples, FR = fast ripples.

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in the Table 1, epileptic hippocampus was selected in 12 patients. A unilateral hippocampal epileptic region was found in 9 patients (6 patients with Engel IA (seizure free), 1 patient with Engel IIA (histologically confrmed hippocampal sclerosis), 2 patients with Engel IIIA (in one case histologically confrmed hippocampal sclerosis)). Bilateral epileptic hippocampal regions were determined in three, in which we analyzed the data only within a more pathologically active hippocampus. Putative NEH were defned by the absence of (a) a seizure onset zone and (b) frequent interictal spikes (IEDs; > 50 per 10 min). Te putative non-epileptic hippocampi with spiking above the threshold were visually reviewed whether the IEDs were propagated from other brain structures. Te putative non-epileptic hippocampi that generated IEDs were excluded from the analysis. NEH were identifed in either the lef or right hippocampus in extramesiotemporal epilepsy, but contralateral to the epileptogenic hippocampus in unilateral mesiotemporal epilepsy. In this way, each hippocampus could be classifed either as epileptic or non-epileptic. We always analyzed all the electrode contacts within a particular hippocampus. In each subject, all the obtained data were reviewed to identify artifactual and pathological traces by expert neu- rologists (M.B. and M.P.). Awake resting state was recorded with the subject’s eyes closed with the minimization of possible external stimuli.

Behavioral tasks. Subjects were seated comfortably in a moderately lighted room. A monitor screen was placed approximately 100 cm in front of their eyes. During the task, they were asked to focus their gaze on a small fxation point in the center of the monitor screen. We performed a standard visual oddball task: three types of stimuli (target, frequent, and distractor) were presented in the center of the screen (black background) for 500 ms in random order at a ratio of 1:4.6:1. Te interstimulus interval varied randomly between 4 and 6 s. Specifcally, the experimental stimuli comprised clearly visible yellow capital letters “X” (target), “O” (frequent), and various other capital letters (distractor). Te number of targets was 50. Te task was divided into four blocks, each block consisting of 12 or 13 target stimuli. Each subject was instructed to count the target stimuli sub- vocally and to report the calculated number afer each block.

Data analysis. Using a modifed pipeline for automated HFO ­detection21, we analyzed potential ripple and fast ripple rates in EH and NEH. We also carried out an automated detection of interictal spikes in the dataset used in this ­study38. Further, we compared the infuence of the cognitive task on HFO and spikes occurrence in EH and NEH. Analyses of the HFO and spike occurrences and HFO features (relative amplitude, duration, and spectral entropy) were performed separately in the frst 10-min window of the visual oddball task (i.e. through- out the whole epoch, not just in the short segments afer specifc stimuli) and during the resting state. Te detector of HFOs utilizes a sequence of power envelopes in consecutive logarithmically spaced frequency bands within a 10 s statistical window. Te z-score of each separate power envelope is computed and a matrix of the z-scored power envelopes is created. Te segment of each power envelope above the threshold (> 3) and with the number of oscillations larger than 1 is marked as a band detection. Band detections overlapping in the temporal domain are joined into one event (Fig. 1). Te relative amplitude is calculated as the highest z-score value of the event and the frequency is determined as the frequency band in which this value occurred. Te duration is derived from the frst and last value above the threshold across frequency bands. Spectral entropy was computed as the entropy of normalized power spectral density of detected events. Only detections longer than 5 oscillations at the determined frequency were processed. To investigate how many HFOs occurred simultaneously with spikes and could be infuenced by the changes in spike rates, we analyzed the number of HFOs that are superimposed on detected spikes. We analyzed the counts of spike-HFOs as well as standalone HFOs.

Statistical analysis. Te statistical analysis was performed using in-house Python scripts. Te individual data sets were frst tested for distribution normality with D’Agostino’s normality test. Subsequently, since most of the data sets were not normally distributed, the Wilcoxon rank-sum test was used to investigate diferences between studied data sets (the average for individual channels were compared statistically). Bonferroni cor- rection for multiple comparisons was applied where necessary. We also performed an analysis of diferences between task-induced and resting HFO rates in each hippocampus (for each contact) using the Wilcoxon paired signed-rank test.

Ethics approval and consent to participate. We confrm that we have read the Journal’s position on the issues involved in ethical publication and afrm that this report is consistent with those guidelines. All subjects gave their written informed consent prior to the investigation. Results Interictal spikes, ripples, and fast ripples were detected in the hippocampi of all subjects included in the study. Te mean percentage of HFOs that occurred simultaneously with spikes was 41% (in detail see Table 2); that is, most HFOs were observed independently of spikes. Te degree of success in completing the cognitive task across all patients was at least 96%; this translates to a maximum of 2 errors out of 50 targets. During the resting period, a comparison of HFO features revealed that both epileptic and non-epileptic hip- pocampi exhibited HFOs with similar properties. In the EH compared with NEH, however, we observed both ripples and fast ripples signifcantly more frequently and with higher relative amplitude and longer duration (Table 3). Furthermore, there was no signifcant diference in HFO spectral entropy. An illustration of these comparisons for ripples and fast ripples are presented in Figs. 2, 3 and 4.

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Figure 1. HFO detection. Raw data (A) and band-pass fltered data in the high gamma band frequency (65– 80 Hz; (B), ripple band (80–250 Hz; (C) and fast ripple band (250–600 Hz; (D,E) Z-scored power envelopes in a series of log spaced band-pass fltered bands. Te time scale of all subplots (A–E) is identical. Te detection is represented by red lines in (A,C,E). Te brightest spot of the detected event in (E) corresponds to the maximum peak relative amplitude. Te frequency of the event is determined by the frequency band in which this peak occurred. Te duration of the event is calculated as the diference between earliest onset and latest ofset across frequency bands.

Epileptic hippocampus Non-epileptic hippocampus Subject Spikes Spike-HFOs Standalone HFOs Spikes Spike-HFOs Standalone HFOs 1 1950 586 766 205 0 371 2 1623 840 2531 – – – 3 4501 2841 838 – – – 4 812 477 347 – – – 5 1735 1012 299 499 254 241 6 1241 138 988 155 0 347 7 2314 832 706 – – – 8 1149 391 1858 – – – 9 2767 1450 2204 – – – 10 636 295 1168 222 30 576 11 815 277 458 – – – 12 – – – 308 6 188 13 – – – 155 5 376 14 – – – 326 168 150 15 3560 1195 1106 – – –

Table 2. Rates of spikes, spike-HFOs and standalone HFOs per 10 min for individuals within resting-state recording.

Te mean HFO rate in the resting period across all contacts was 219.9 ± 151.6 and 40.1 ± 44.3 per 10 min within EH and NEH, respectively. In cognitive-task periods, the mean HFOs rate within EH and NEH changed to 95.5 ± 82.9 and 43.3 ± 19.0 per 10 min, respectively. Te change of HFO rate during the cognitive task was signifcant in EH (p < 0.001) but not in NEH. Similar results were revealed by statistical analysis of the difer- ences between the task-induced and the resting period HFO rates using Wilcoxon signed-rank test: A signifcant reduction of HFO was observed only in contacts within EH (p < 0.001). HFO rates were signifcantly diferent in EH compared to NEH during resting state as well as during the cognitive task (p < 0.001 and p = 0.002, respectively). Looking at HFOs separately in the ripple and fast ripple frequency ranges, we obtain similar results. Ripple and fast ripple rates were signifcantly reduced during the

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Rate (N/10 min) Log relative amplitude Duration (ms) Spectral entropy Rest Oddball p value Rest Oddball p value Rest Oddball p value Rest Oddball p value Ripples 45.73 38.39 NEH 16.06 (± 16.57) 9.0 (± 6.74) Nonsig 1.92 (± 0.30) 1.95 (± 0.22) Nonsig Nonsig 4.32 (± 0.51) 4.71 (± 0.25) 0.001 (± 10.83) (± 13.58) 125.50 54.96 52.71 53.06 EH < 0.001 2.32 (± 0.21) 2.25 (± 0.27) Nonsig Nonsig 4.46 (± 0.29) 4.61 (± 0.35) < 0.05 (± 75.91) (± 52.29) (± 10.97) (± 14.12) p value < 0.001 < 0.001 < 0.001 < 0.001 < 0.05 < 0.001 Nonsig Nonsig Fast ripples 34.54 19.14 NEH 28.52 (± 22.09) Nonsig 1.89 (± 0.23) 1.88 (± 0.12) Nonsig 16.40 (± 5.31) Nonsig 4.82 (± 0.39) 4.89 (± 0.22) Nonsig (± 18.82) (± 10.90) 41.26 27.51 EH 94.38 (± 93.88) < 0.001 2.16 (± 0.27) 2.02 (± 0.25) < 0.005 21.75 (± 8.66) < 0.005 4.65 (± 0.34) 4.87 (± 0.34) < 0.001 (± 39.47) (± 11.55) p value < 0.005 Nonsig < 0.001 < 0.01 < 0.01 < 0.01 Nonsig Nonsig

Table 3. HFO characteristics per contact in the ripple and fast ripple ranges during rest and the oddball task.

Figure 2. Fast ripple and ripple rates during resting and cognitive-task periods within the epileptic and non- epileptic hippocampi across all investigated subjects. Black asterisks indicate signifcant diferences in epileptic hippocampi (p < 0.001). Black diamonds indicate outliers.

cognitive task in EH only, however (p < 0.001); the rate of HFOs was not signifcantly infuenced by the cognitive task in NEH (Fig. 2). During the cognitive task, HFOs (both ripples and fast ripples) were detected with the same features as during the resting period in epileptic and non-epileptic hippocampi. However, ripples exhibited higher spectral entropy in both EH and NEH during the cognitive task compared with the resting period. Te relative amplitude and duration of R did not change in neither NEH nor EH. During performance of the cognitive task, more HFOs in the fast ripple frequency range with lower relative amplitude, shorter duration, and higher spectral entropy were detected in EH than during the resting period. In NEH, all characteristics of fast ripples did not difer signifcantly between rest and the cognitive task. Te comparisons of the specifc HFO characteristics in the ripple and fast ripple ranges are summarized in Table 3. Spikes exhibited a similar signifcant decrease in their rate during the cognitive task in the epileptic hippocam- pus, but this was observed also in non-epileptic hippocampus. Counts of spike-HFOs as well as standalone HFOs also showed signifcant reductions during the cognitive task in the epileptic hippocampi (Table 4).

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Figure 3. Ripple duration, relative amplitudes and spectral entropy during the resting period within the epileptic and non-epileptic hippocampi across all subjects. Black asterisks indicate signifcant diferences (p < 0.001). Black diamonds indicate outliers.

Figure 4. Fast ripple duration, relative amplitudes and spectral entropy during resting periods within the epileptic and non-epileptic hippocampi across all investigated subjects. Black asterisks indicate signifcant diferences (p < 0.001). Black diamonds indicate outliers.

Discussion Widespread cortical and subcortical neuronal networks are thought to be coordinated into synchronous oscilla- tions spanning ripples or fast ripples frequency ranges during cognitive phenomena but also pathologic epileptic processes­ 20. In this study, we investigated HFOs only in the hippocampus, which plays a pivotal role in both cognitive (especially learning and ) and epileptogenic processes and which is the most studied brain structure in relation to HFOs. Unsurprisingly, we observed that the ripples and fast ripples were detected at relatively low rates in NEH but at much higher rates in the EH (seizure onset zone), which confrms previously published data concerning the pathogenicity of this phenomena­ 4,18,39,40. Importantly, our results not only confrm

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Spikes Spike-HFOs Standalone HFOs Rate (N/10 min) Rate (N/10 min) Rate (N/10 min) Rest Oddball p value Rest Oddball p value Rest Oddball p value 33.88 38.88 NEH 27.15 (± 18.86) 14.13 (± 13.57) < 0.01 0.72 (± 0.97) 0.88 (± 1.76) Nonsig Nonsig (± 29.69) (± 19.34) 187.9 114.00 98.68 41.25 122.51 56.88 EH < 0.001 < 0.001 < 0.001 (± 120.34) (± 83.59) (± 77.77) (± 47.30) (± 122.65) (± 68.94) p value < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Nonsig

Table 4. Spikes, spike-HFOs and standalone HFOs rates per contact during rest and the oddball task.

our previous fndings of signifcantly diferent behavior of ripples within the EH and NEH, suggesting diverse mechanisms of their ­generation8, but also extend these previous fndings by revealing similar results in the fast ripples frequency range. Our results suggests that a distinction between epileptic and non-epileptic hippocampus cannot be based solely on HFO rates or characteristics at rest; there is no clear limit of HFO rates per 10 min of recording, nor any HFO characteristics that could be used to classify the hippocampal tissue surrounding individual contacts as epileptic or non-epileptic. Only during the very specifc discriminative task did we observe a diferential decrease in the rate of HFOs in EH. Our results observed within epileptic hippocampi show that its activity is modifed by the cognitive task and confrm that a specifc discriminative task suppresses pathological HFOs in the epileptic hippocampus, which we discuss below. Hippocampal and parahippocampal physiological ripples have been proposed to be functionally involved in memory consolidation, strengthening and reorganizing memory traces during both rest and slow wave sleep and providing a link between information transfer and memory formation­ 20,41–44. However, this concept of rip- ples as a physiological phenomenon in the hippocampus during memory-related memory processes has been questioned several ­times8,12. Our previous study showed a signifcant decrease of ripple rate in epileptic hip- pocampus during event processing. Tis may suggest increased involvement of normal hippocampal neurons in physiological cognitive processing and reduced involvement in the epileptic network impelled by synchronously bursting ­neurons8. Te prevalence of pathologic ripples is seen usually during non-REM sleep, likely resulting from the sleep-dependent enhancement of network synchronization­ 5,45–47. Tis suggests that a proportion of ripples present in EH are connected to underlying pathological network ­activity8. Our observation that a cogni- tive task only partly afects general ripple rate within NEH (a large overlap was observed between both EH and NEH) may be explained by the expected physiological role of normal hippocampal neurons during both rest (memory consolidation/awake neuronal replay) and task (complex event discrimination processing) periods­ 8. Conversely, hippocampal fast ripples, VFR, and UFR have been repeatedly reported and considered as bio- markers of epileptogenesis and epileptogenicity, related to pathological processes and occurring in close proxim- ity to the epileptic focus­ 1,9,10. VFR and UFR seem to be more localized to the epileptogenic zone than fast ripples; surgical removal of the tissue generating these interictal HFOs leads to favorable surgery ­outcome9. Although fast ripples are considered pathological, they were also detected in a non-epileptic ­hippocampus48. Based on our results, both epileptic and non-epileptic hippocampi have a population of fast ripples with similar proper- ties (i.e. with low fast ripple counts, low relative amplitude, short duration, and non-signifcant higher spectral entropy), but in the epileptic hippocampus, higher rates of HFOs with extra values of properties tend to occur more ofen. In other words, the variance is much larger for all the HFO features measured in EH than in NEH. In line with published data, by evaluating fast ripple occurrence in resting and active periods, we observed the rates of fast ripples spreading to much higher values in EH, and a decreasing number of fast ripples in EH during the discriminative task processing. A similar mechanism like ripple range could be supposed, i.e. the increased involvement of preserved normal hippocampal neurons that are active in some physiological cognitive process- ing and the reduced involvement of synchronously bursting neurons within the epileptic network generating pathological HFOs­ 8. Since the degree of success in completing the cognitive task in all patients was at least 96%, it can be assumed that the changes observed in task performance are really related to the mental processing dur- ing the cognitive task. Te observed HFO changes in the epileptic hippocampus during the cognitive paradigm could partially refect the result of activity in many neuronal networks and cognitive processes, including e.g. attention, conscious processing of an event, , stimulus evaluation and response ­preparation49,50. Terefore, the HFO changes observed in the epileptic hippocampus during the oddball task cannot be assigned to a specifc function but rather generally related to mental processing. According to published studies, most brain cortical areas react (with task-induced modulation of high fre- quency activity) to at least one of the cognitive tasks performed by the patient­ 51, except for brain epileptogenic regions that are heavily contaminated by epileptiform activity­ 51. Similarly, in a recent animal study, the authors revealed that pathological HFO rate is independent of brain state, though they did not test ­ 52. Based on these results, we would expect that the occurrence of interictal spikes and HFOs is unlikely to be altered during state changes or by stimulation in the epileptic hippocampal region, compared to areas not responsible for seizure generation. Nevertheless, we observed changes just in EH. Tis would suggest that the epileptic hippocampus was actually participating in processing the stimulus. It is very well known that even within the epileptic hip- pocampi, some portion of physiological cognitive functions is ofen preserved. Ewell´s group confrmed that both pathological and non-pathological HFOs can co-occur in the same memory circuits and moreover, up to 28% CA1 principal cells participate in generating both ­events52. However, as can be seen, the majority of hippocampal

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neurons are modulated by only one event type or the other­ 52. Besides clinical and neuropsychological indices, several studies with intracerebral event-related potentials detected cognitive P3 phenomena in both normal and epileptic ­hippocampi53–55. In epileptic hippocampi, these ERPs are ofen changed but not completely missing. As mentioned already, in our study fast ripples were also observed in NEH distant from the epileptogenic zone. Te fast ripples detected in NEH cannot be clearly defned as pathological or physiological or as a mani- festation of the propagation of pathological HFO generated elsewhere. But this propagation efect will not play a signifcant role, as SEEG measures the local feld potentials generated within a centimeter radius and the feld formed by neurons over a centimeter from a recording site contribute only a marginal part of the ­signal56. Moreover, which is essential, the cognitive task obviously did not change the rate of HFOs in NEH. Tis of course raises the question of whether fast ripples are the result of the non-epileptic activity of neurons in the NEH, since the fast ripple number did not change between resting state and cognitive task. If there were only pathological fast ripples within NEH, we assume that their number would decrease during a cognitive task, similarly to what we found in EH. Not observing a signifcant change in HFO rate within non-epileptic hippocampi, we hypothesize that healthy hippocampal neurons activated in our specifc cognitive task are not involved in physiological HFO genesis. It is widely accepted that physiological HFOs are refecting memory consolidation and are much more active during ­sleep20,41–44; this is the opposite of our task, which demands a very high attentional load. Actually, the phenomenon of physiological fast ripples (up to 600 Hz) induced in the hippocampus during a cognitive task was also described in a recent study by Kucewicz et al.21. Te number of induced HFO was decreas- ing with increasing frequencies. Most of these induced oscillations lasted between 10 and 25 ms, similarly to the gamma cycle synchronization time frame (correlating with memory formation, loading and maintenance) and the time window for synaptic interactions of neuronal ­ensembles21. Tis fnding supports a physiological origin of fast ripples also in the hippocampus, not only within the primary motor cortex, somatosensory cortex, and visual cortices as was previously published­ 23,24,26,57. All things considered, Kucewicz et al.21 hypothesized that these induced both ripples and fast ripples likely refect the coordinated activity of a number of stimulus-specifc neurons responding to stimuli. Finally, these phenomena play an important role for fast network synchroniza- tion in human cognition. Concerning the HFO entropy, we found that fast ripples had higher entropy during oddball task than in the rest in the EH. Our results are congruent with those published recently by Liu et al.58. Consistently, in all patients, the typical HFOs with the highest degree of waveform similarity (in our case, we can use the term “low entropy”) were seen within epileptogenic tissue only, whereas HFOs embedded in random waveforms (high entropy) were generated by sites in the functional regions independent from the epileptogenic locations­ 58. Te repetitive waveform pattern was evident in fast ripple range also in our data. Tis result confrms the possibil- ity of physiological fast ripples in NEH and reduced pathological FR in EH, since these oscillations had higher entropy than those seen during rest in EH and therefore probably have a diferent origin. Additionally, we have shown that the “physiological” HFOs have signifcantly diferent properties from “path- ological” HFOs, primarily shorter duration and lower ­amplitude21,24. In line with this, during the cognitive task in EH the fast ripples were detected with lower mean relative amplitude and shorten duration. In summary, during the cognitive task, more fast ripples were observed in EH with similar characteristics as in the NEH. Te fundamental question remains of whether the resting state and task-related ripples and fast ripples (normal and pathological) exhibit similar or diferent mechanisms of generation or possess any functional sig- nifcance. As was shown, HFOs can simply represent a marker of highly activated and synchronized neurons, regardless of the structure or mechanism underlying them. Tese high frequency signals appear to aggregate local (spiking) activity of neuronal populations or network oscillations­ 59. However, the spectral content of local feld potential oscillations, which refects high spectral components arising from sharply contoured transients, is not considered true/standalone HFOs in the invasive EEG­ 36,60. Neurons fring broader spikes contaminate the local feld potential to a greater extent because their waveforms have stronger components in lower frequencies than short spikes. Moreover, neurons that fre coupled to a certain rhythm and spike synchrony can increase the extent of spike contamination­ 34. Signals occurring over larger spatial extents are expected to have greater efect on high frequencies and contribute to a broader range of frequencies­ 34. In our study we analyzed true HFOs in the majority of cases as the mean percentage of HFOs that occurred simultaneously with spikes was only 35% (46% in EH and 6% in NEH). We also analyzed the counts of spike-HFOs as well as standalone HFOs. Both analyses showed signifcant reduction during the cognitive task in the epileptic hippocampus. Physiological HFOs result from phasic inhibitory input on the soma of pyramidal cells, while epileptic HFOs, usually superimposed on interictal epileptiform sharp waves, appear to refect the feld potentials which are formed by the activity from clusters of abnormal synchronously bursting pyramidal cells, generating population spikes, and decreased inhibitory interneuron fring­ 6,15. Based on the functioning of synaptic transmission, the contribution of this mechanism to HFO genesis is limited to approximately 80–150 Hz61. Te true high frequency local feld potential oscillations above ~ 250 Hz are above the physiological fring rate of pyramidal neurons and cannot be generated by synaptic ­currents61. It is assumed, these local feld potential oscillations are an arising rhythm generated by the in and out of phase action potential fring of populations of neuronal cell assemblies or ­clusters9,61. Originally suggested pathologically interconnected neurons emitting hypersynchronous bursts, as the source of fast ripple ­oscillations15 and a population of these clusters might underlie generation of activity above ~ 500 Hz9. Te fact that our study consists of analyses of chronic epileptic patients is an obvious limitation. Te brain tis- sue from which the signal is acquired is not organized in the same way as normal tissue; this may lead to a bad and misleading model of physiological human neural processing and functional organization­ 56. Te possibility of the disease-related processes interfering with the reported physiological oscillations cannot be completely ruled out

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and must be taken into account, although usually many epileptic patients perform behaviorally as well as normal ­subjects56. Certain caution must be taken when interpreting “normal” results onto normal hippocampal behavior. It is important to highlight that the majority of previous studies evaluated results drawn from HFO analysis at a group level, and when considering individual patients the rates of HFOs are ofen highly variable and less specifc for epileptic brain localization­ 12,36. HFOs could refect increased cortical excitability, perhaps more than epileptogenicity. Our results support a possible physiological origin of fast ripples as well. Tus, in individual patients, the count of fast ripples may include fast ripples of physiological origin and therefore fast ripples may not be a sensitive and unique biomarker of ­epileptogenicity12. Tis fnding, however, does not alter the fact that pathological fast ripples clearly prevail in epileptic hippocampi. Conclusion Based on our results using a visual oddball task, it is possible to diferentiate between epileptic and non-epi- leptic hippocampi, even though both hippocampi have HFOs with similar features that probably refect non- pathological phenomena. And so, fast ripples recorded in the hippocampus should not be considered as only a pathological. Our results confrm the distinct impact of a very specifc discriminative task processing on ripples and fast ripples within epileptic and non-epileptic hippocampi, particularly the suppression of pathological HFOs in epileptic hippocampus.

Received: 27 December 2019; Accepted: 23 September 2020

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Acknowledgements Te results of this research were acquired within the CEITEC 2020 (LQ1601) project, with fnancial support from the Ministry of Education, Youth and Sports of the Czech Republic (MEYS CR) within special support paid from the National Programme for Sustainability II funds. Supported by MEYS CR project (Nos. LH15047, KONTAKT II). Supported by funds from the Faculty of Medicine MU to junior researcher (Martin Pail). Author contributions M.P., J.C., R.R., and M.B. contributed to the conceptualization of the study. M.P., R.R., P.D., J.Ch. were responsible for project administration. M.P., R.R., J.C., J.Ch., P.D. were responsible for data curation. M.P., R.R., D.J.S. and M.B. wrote the main manuscript text. All authors reviewed and approved the fnal manuscript.

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